The Clinical Utility of Automated Immature Granulocyte Measurement in the Early Diagnosis of Bacteremia.

Background Early diagnosis and prompt management are crucial for bacteremia and sepsis, as they have the potential to lead to septic shock and fatal outcomes. Bacteremia induces the recruitment of immature granulocytes (IGs) into the circulation, which indicates active bone marrow response. The goal of our present study is to determine the effectiveness of automated IG measurement as an alternate indicator for infection and also its clinical utility in predicting positive blood culture (BC) results. Methods We conducted a retrospective study including 100 BC-positive patients for whom complete blood count (CBC) and BC were done at the same time. Multiple hematological parameters including total white blood cell count (TWC), absolute neutrophil count (ANC), absolute lymphocyte count (ALC), IG count (IGC), and IG percentage (IG%) were obtained from the automated hematology analyzer, and IGC/TWC (IG ratio), IGC/ANC (immature-to-total neutrophil ratio), and ANC/ALC (neutrophil-to-lymphocyte ratio) were calculated using the primary data and compared with 100 uninfected normal individuals. Results The mean value of IG% and IGC between culture-positive and culture-negative groups were statistically significant (p-value < 0.05), suggesting that they are potential markers for bacteremia, and also the IG% was significantly higher in patients with positive BCs. Conclusion IG measurement is an easily accessible, cost-effective potential marker for screening bacteremia. Therefore, IGC and IG% could be incorporated as a part of the CBC report.

Deep Learning Approaches for Cyberbullying Detection and Classification on Social Media.

As a result of the ease with which the internet and cell phones can be accessed, online social networks (OSN) and social media have seen a significant increase in popularity in recent years. Security and privacy, on the other hand, are the key concerns in online social networks and other social media platforms. On the other hand, cyberbullying (CB) is a serious problem that needs to be addressed on social media platforms. Known as cyberbullying (CB), it is defined as a repetitive, purposeful, and aggressive reaction performed by individuals through the use of information and communication technology (ICT) platforms such as social media platforms, the internet, and cell phones. It is made up of hate messages that are sent by e-mail, chat rooms, and social media platforms, which are accessed through computers and mobile phones. The detection and categorization of CB using deep learning (DL) models in social networks are, therefore, crucial in order to combat this trend. Feature subset selection with deep learning-based CB detection and categorization (FSSDL-CBDC) is a novel approach for social networks that combines deep learning with feature subset selection. The suggested FSSDL-CBDC technique consists of a number of phases, including preprocessing, feature selection, and classification, among others. Additionally, a binary coyote optimization (BCO)-based feature subset selection (BCO-FSS) technique is employed to select a subset of features that will increase classification performance by using the BCO algorithm. Additionally, the salp swarm algorithm (SSA) is used in conjunction with a deep belief network (DBN), which is known to as the SSA-DBN model, to detect and characterize cyberbullying in social media networks and other online environments. The development of the BCO-FSS and SSA-DBN models for the detection and classification of cyberbullying highlights the originality of the research. A large number of simulations were carried out to illustrate the superior classification performance of the proposed FSSDL-CBDC technique. The SSA-DBN model has exhibited superior accuracy to the other algorithms, with a 99.983 % accuracy rate. Overall, the experimental results revealed that the FSSDL-CBDC technique beats the other strategies in a number of different aspects.

A stacked convolutional neural network for detecting the resource tweets during a disaster.

Social media platform like Twitter is one of the primary sources for sharing real-time information at the time of events such as disasters, political events, etc. Detecting the resource tweets during a disaster is an essential task because tweets contain different types of information such as infrastructure damage, resources, opinions and sympathies of disaster events, etc. Tweets are posted related to Need and Availability of Resources (NAR) by humanitarian organizations and victims. Hence, reliable methodologies are required for detecting the NAR tweets during a disaster. The existing works don't focus well on NAR tweets detection and also had poor performance. Hence, this paper focus on detection of NAR tweets during a disaster. Existing works often use features and appropriate machine learning algorithms on several Natural Language Processing (NLP) tasks. Recently, there is a wide use of Convolutional Neural Networks (CNN) in text classification problems. However, it requires a large amount of manual labeled data. There is no such large labeled data is available for NAR tweets during a disaster. To overcome this problem, stacking of Convolutional Neural Networks with traditional feature based classifiers is proposed for detecting the NAR tweets. In our approach, we propose several informative features such as aid, need, food, packets, earthquake, etc. are used in the classifier and CNN. The learned features (output of CNN and classifier with informative features) are utilized in another classifier (meta-classifier) for detection of NAR tweets. The classifiers such as SVM, KNN, Decision tree, and Naive Bayes are used in the proposed model. From the experiments, we found that the usage of KNN (base classifier) and SVM (meta classifier) with the combination of CNN in the proposed model outperform the other algorithms. This paper uses 2015 and 2016 Nepal and Italy earthquake datasets for experimentation. The experimental results proved that the proposed model achieves the best accuracy compared to baseline methods.

Analysis of heavy metals in dying wetland Pallikaranai, Tamil Nadu, India.

Pallikaranai wetland has high ecological significance as it has been a home for other associated biodiversities. This wetland is highly polluted due to the rapid industrialization, urbanization and dumping of solid waste. The water quality of the Pallikaranai wetland has been studied with reference to toxic metals. The metals analyzed include lead, chromium, iron, copper, nickel, zinc and cadmium. The heavy metal analysis in surface waters were in the following range; Cd: BDL--0.019 mg l(-1), Fe: BDL--1.52 mg l(-1), Cu: BDL--0.02 mg l(-1), Ni: BDL-0.60 mg l(-1), Pb: 0.03-1.13 mg l(-1), Zn: 0.002-0.14 mg l(-1) and Cr: 0.10-1.52 mg l(-1) respectively. The dominance of various heavy metals in the surface water of the Pallikaranai wetland followed the sequence: Pb > Cr > Fe > Ni > Zn > Cd > Cu. The quality of water has deterioted due to the various anthropogenic activities. Most of the metal ions were in higherconcentration compared to the standards. It has been observed that the quality of the surface water is not safe for aquatic and domestic life, hence necessary management actions should be taken to control the quality of the surface water.